?MODIFIED PROJECT SUMMARY/ABSTRACT Most patients with type 2 diabetes are prescribed medications to lower glucose levels and reduce the risk of long-term complications. Severe hypoglycemia (SH) occurs as an unintended consequence of medications (i.e., an iatrogenic effect) and is defined as a low blood glucose level for which the patient requires assistance. SH is associated with poorer quality of life, serious falls, car accidents, ventricular arrhythmia, dementia, hospitalizations, and a several-fold increased risk of death. Despite advances in pharmacotherapy, SH leading to emergency department (ED) visits or hospitalization has emerged as one of the most prevalent complications of diabetes treatment and is a critical public health concern. One in four emergency hospitalizations for adverse drug events among older adults is due to SH. Yet, we lack sufficiently robust ascertainment tools to estimate the total incidence of SH. Despite increasing treatment complexity and prevalent polypharmacy, we also lack a reliable understanding of how SH risk changes when patients initiate combinations of glucose-lowering therapies or their interactions with non-diabetic drugs. We propose to study SH in a large (n~229,000), diverse cohort of adults with type 2 diabetes from an integrated healthcare delivery system (Kaiser Permanente Northern California or KPNC) with these 3 specific aims: In Aim 1, we will develop and validate novel algorithms for identifying SH from EMR data (ED and hospital). Using these EMR coding algorithms, we will conduct comprehensive surveillance of trends in clinically recognized SH. We will also estimate the proportion of SH not clinically recognized (based on linkage with self-reported SH from a previous survey), which will facilitate calculation of the total rates of SH. In Aim 2, we will quantify the change in risk of SH associated with initiation of medications commonly used by people with diabetes (i.e., examining the effect of diabetes medications, non-diabetes medications, or their interactions), using rigorous, causal modeling techniques (e.g., difference-in-difference analysis, marginal structural models, directed acyclic graphs). In Aim 3, we will estimate whether the change in SH risk associated with initiation of medications (from Aim 2) differs substantively across specific risk subgroups (e.g., prior SH, CKD, long duration diabetes, the elderly), and by baseline and changes in HbA1c. This study will 1) provide new SH ascertainment tools to improve surveillance nationwide and thereby enable a more comprehensive understanding of SH epidemiology; and 2) provide valid estimates of change in SH risk associated with initiation of medications commonly used by people with diabetes, thus helping providers and patients individualize diabetes management while minimizing the risk of SH.